1. Twitter in India & the JNU Protests

2. Scraping Twitter

3. Preparing a Network Analysis

4. Analysing the #JNUProtest Network

5. Questions

• Twitter is used in India as an arena or even a battlefield between the “Hindu Nationalists” and “Secular liberals” (Udupa 2019)
• Only a distinct group has access and actively uses Twitter: the Indian (urban) middle class. Twitter helps them to make political claims to the moral and political hegemony in India

• Twitter can be used as an excellent and easy accessible data source for your research
• Qualitative approach: the focus lies on the content of the tweet, the language and the themes and categories that we can identify
• When we see the tweet as text, we can analyse the discourse linked to our research question

• For today’s presentation we have chosen a recent event: JNU students protest in Delhi against a steep hike in hostel fees and the limitation of their freedom of movement within the campus; the protest was beaten down by the police in Delhi
• we have chosen two hashtags that were trending at the time to collect our data: #EmergencyinJNU and #JNUprotest

https://twitter.com/t_d_h_nair/status/1196367277102264320

• In India, the JNU stands for the secular and liberal India; in the past it has been seen as a “training ground” for future politicians mostly from left-wing parties
• JNU campus itself is quite a mythical place for most students: here they can study and take part in political discussions for the first time; for many, the JNU has been an escape from the closed and tabooing Indian society (no dating or pre-marital relationships, etc.)
• For Hindu nationalist activists, JNU and its students and professors symbolize the “enemy”, they embody the anti-national, secular and liberal India that oppose their aim to transform India into a Hindu state

• API = Aplication programming interface. Gives access to the functions of the platform
• API access can be applied for via https://developer.twitter.com/en/apply-for-access
• Requires a twitter account
• During the application, reasons for the API use must be submitted
• Academic use is usually granted access. Make sure, however, not to violate Twitter's terms of use in the description of your project
• Other platforms, like youtube or facebook, have APIs that can be used for scraping as well
  (In case of Facebook, however, access got extremely retricted after Cambridge Analytica)

Access to the Twitter API gives you an access token that needs to be loaded into R when scraping

load("twittertoken.RDa")

• R is a programming language with an easy-to-use interface called R Studio
• It is free and open source
• Mostly used for statistical applications, but very flexible
• Additional functionality is provided by user-devloped packages
• Popular among Social Scientists

library(rtweet)

Search

search <- search_tweets(q = "#JNUProtests OR EmergencyinJNU", 
                        n = 1000, 
                        token = twitter_token)
View(search)

• basically works like the search on Twitter
• returns a maximum 18.000 tweets per call from up to 7 days ago
• rtweet also allows to get user data, like followers or timelines, with its different functions

library(igraph)
library(graphTweets)

load("JNUProtest_search.RDa")

search %>%
  gt_edges(screen_name, mentions_screen_name) %>%
  gt_nodes() %>%
  gt_graph() -> mentions_graph

• Users become Nodes in the network
• Mentions (@s, Retweets, Comments, Replies) become Edges between Nodes

• Gephi (https://gephi.org/) is a great, free and easy to use tool for netowrk visualisation and analysis

• Standard R dataformat is not supported, we have to export our Graph

library(rgexf)

nodes_df <- data.frame(ID = c(1:vcount(mentions_graph)), 
                       NAME = V(mentions_graph)$name)
edges_df <- as.data.frame(get.edges(mentions_graph, 
                       c(1:ecount(mentions_graph))))
edges_df$n <- E(mentions_graph)$n

write.gexf(nodes = nodes_df[1:2], 
           edges = edges_df[1:2], 
           edgesWeight = edges_df$n, 
           defaultedgetype = "directed", 
           output = "workshop_mentions_graph_2.gexf")

Send us an email to
erikbecker@hotmail.de
or
tim.koenig@wzb.eu

Bennett, W. Lance, und Barbara Pfetsch. 2018. „Rethinking Political Communication in a Time of Disrupted Public Spheres“. Journal of Communication 68 (2): 243–53. https://doi.org/10.1093/joc/jqx017.

Bruns, Axel, und Jean Burgess. 2012. „Researching News Discussion on Twitter“. Journalism Studies 13 (5–6): 801–14. https://doi.org/10.1080/1461670X.2012.664428.

Luke, Douglas A. 2015. A User’s Guide to Network Analysis in R. Use R! Springer International Publishing. https://doi.org/10.1007/978-3-319-23883-8.

Maireder, Axel, und Stephan Schlögl. 2014. „24 Hours of an #outcry: The Networked Publics of a Socio-Political Debate“. European Journal of Communication 29 (6): 687–702. https://doi.org/10.1177/0267323114545710.

Meraz, Sharon, und Zizi Papacharissi. 2013. „Networked Gatekeeping and Networked Framing on #Egypt“. The International Journal of Press/Politics 18 (2): 138–66. https://doi.org/10.1177/1940161212474472.